Instructional Material
Here Are Free AI Learning Resources For Beginners - Analytics India Magazine
Given how artificial intelligence is a buzzing topic, it has sparked a slew of beginner-friendly introductory resources that clear the general concepts from this very broad topic. And for most newcomers, the most interesting topic in AI is Deep Learning. In fact, Google's Python-based Deep Learning framework Tensorflow has helped many a developer get up to speed with the technical concepts. Besides videos and free online courses, you must also have a reading list that helps you cover the math and statistics behind the algorithms. While YouTube videos remain the main learning source and a key starting point for beginners, there is a slew of resources, especially books that can help cement fundamental concepts.
Free 29-part course to learn Machine Learning – Hacker Noon
Like Mathematics and Computer Science, it is quickly becoming a tool which is widely used to make everything more effective and efficient, ranging all the way from websites to medical diagnosis. Today, I'm happy to announce the free Machine Learning Course on Commonlounge. Apart from tutorials on ML concepts and algorithms, the course also includes end-to-end follow-along examples, quizzes, and hands-on projects. Once done, you will have an excellent conceptual and practical understanding of machine learning and feel comfortable applying machine learning thinking and algorithms in your projects and work. This tutorial introduces what machine learning is.
Best TensorFlow videos, courses & tutorials 2018 - ReactDOM
Complete Guide to TensorFlow for Deep Learning with Python by Jose Portilla will help you learn how to use Google's Deep Learning Framework, TensorFlow with Python. This Deep Learning TensorFlow course is for Python developers who want to learn the latest Deep Learning techniques with TensorFlow. You will understand how Neural Networks work. Then you will build your own Neural Network from scratch with Python. This Deep Learning TensorFlow tutorial will teach you to use TensorFlow for Classification and Regression Tasks.
The 13th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment
Magerko, Brian (Georgia Institute of Technology) | Bahamón, Julio César (University of North Carolina at Charlotte) | Buro, Michael (University of Alberta) | Damiano, Rossana (University of Turin) | Mazeika, Jo (University of California, Santa Cruz) | Ontañón, Santiago (Drexel University) | Robertson, Justus (North Carolina State University) | Ryan, James (University of California, Santa Cruz) | Siu, Kristin (Georgia Institute of Technology)
The 13th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE 2017) was held at the Snowbird Ski and Summer Resort in Little Cottonwod Canyon in the Wasatch Range of the Rock Mountains near Salt Lake County, Utah. Along with the main conference presentations, the meeting included two tutorials, three workshops, and invited keynotes. This report summarizes the main conference. It also includes contributions from the organizers of the three workshops.
AAAI News
While artificial intelligence AAAI-19 will comprise a host of programs, well as strong outreach programs for including the Senior Member (AI) and human-computer interaction students, women, and sister conferences. Track, the Technical Demonstration (HCI) represent traditional They have absorbed all former Program, the Tutorial and Workshop mainstays of the conference, HCOMP special tracks into the main conference Programs, and several student programs, believes strongly in inviting, fostering, technical program, with provision for such as the Student Abstract and promoting broad, interdisciplinary distinguished oversight of reviews for and Poster Program and the Doctoral research. This field is particularly these areas.
Pedagogical Agents: Back to the Future
Johnson, W. Lewis (Alelo Inc.) | Lester, James C. (North Carolina State University)
Back in the 1990s we started work on pedagogical agents, a new user interface paradigm for interactive learning environments. Pedagogical agents are autonomous characters that inhabit learning environments and can engage with learners in rich, face-to-face interactions. Building on this work, in 2000 we, together with our colleague, Jeff Rickel, published an article on pedagogical agents that surveyed this new paradigm and discussed its potential. We made the case that pedagogical agents that interact with learners in natural, life-like ways can help learning environments achieve improved learning outcomes. This article has been widely cited, and was a winner of the 2017 IFAAMAS Award for Influential Papers in Autonomous Agents and Multiagent Systems (IFAAMAS, 2017). On the occasion of receiving the IFAAMAS award, and after twenty years of work on pedagogical agents, we decided to take another look at the future of the field. We’ll start by revisiting our predictions for pedagogical agents back in 2000, and examine which of those predictions panned out. Then, informed what we have learned since then, we will take another look at emerging trends and the future of pedagogical agents. Advances in natural language dialogue, affective computing, machine learning, virtual environments, and robotics are making possible even more lifelike and effective pedagogical agents, with potentially profound effects on the way people learn.
A Tutorial to AI Ethics - Fairness, Bias & Perception
Negative: "I hate it", "It scares me" & "I am uncomfortable with it" Positive" "I am comfortable with it", "I am enthusiastic about it" & "I love it". Negative: "I hate it", "It scares me" & "I am uncomfortable with it" Positive" "I am comfortable with it", "I am enthusiastic about it" & "I love it". COMPAS software to generate several scores including predictions of "Risk of Recidivism" and "Risk of Violent Recidivism." COMPAS software to generate several scores including predictions of "Risk of Recidivism" and "Risk of Violent Recidivism." Digital twin refers to a digital replica of physical assets, processes and systems that can be used for various purposes.
Best R tutorials, courses & books 2018 - ReactDOM
R is an open source programming language and software environment for statistical computing and graphics. R was created in 1992 and is supported by the R Foundation for Statistical Computing. R is widely used among statisticians and data miners for data analysis. The popularity of R has increased in the recent years. Here's a list of the best R tutorials, books and courses to help you learn R programming language in 2018.